Archive | IIoT

53

8:52 pm
March 16, 2017
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Intelligent Water Making Strides towards Predictive Analytics

EXCEL XR metering pumps are designed for the specific chemical pumping requirements of municipal and industrial water treatment.

Last week, I ran across a Smart Water spending forecast from Bluefield Research and this week there’s an interesting post from Jim Gillespie, co-founder of Gray Matter Systems, a system integrator for cloud solutions and predictive analytics. All signs point to an increased spend in this sector for pump and motor sensors, but where will this investment come from?

According to Gillespie and his post on TechCruch, utilities may be able to sell “solutions” to other wastewater operations like the power industry has done. Gillespie cited how the District of Columbia Water and Sewer Authority has commercialized their intellectual property, giving them a new revenue channel. The water district is commercializing their water ammonia versus nitrate algorithm and selling it other treatment plants, according to Gillespie.

>> More || Smart Water Infrastructure Continues to Grow, but Real Challenges Persist

As I noted last week, new investment dollars are hard to come by but there’s are a lot of new use cases in the wastewater space, see below:

Another IIoT development, a new SaaS application that’s set to launch later this month, will calculate wastewater clarifier tank performance — providing quick analysis on a critical step in the wastewater process. The tool, called ClariFind, alerts utilities as they’re getting close to a failure before they experience it. ClariFind will predict when sludge will overflow and be released. This kind of problem causes EPA issues and fines that can run in the millions of dollars. It will also be able to predict a thickening failure, which is when the effluent doesn’t settle correctly and creates a costly sludge blanket in the tank. ClariFind is just one part of a water operations suite of productivity enhancers — solutions as a service.

Read the Full Post on TechCrunch >>


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58

12:45 pm
March 8, 2017
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Smart Water Infrastructure Continues to Grow, but Real Challenges Persist

smart water markets

The US (39 projects) and the UK (21 projects) were the most active smart water markets during the last half of 2016. Source: Bluefield Research

By Grant Gerke, Contributing Writer, IIoT

A new report from Bluefield Research suggests that a massive smart infrastructure buildout is coming to the water and wastewater industry in the next eight years, with more than $20 billion to be spent in metering, data management, and analytics.

As devices, sensors and cloud solutions become cheaper over the next ten years, there will be a solid investment in this space but the research rings a little hollow to me. The U.S. industry, in particular, is aging and resources are limited but the big challenge may be in the area of system integrators. In a feature article from a couple years ago, I interviewed Roger Knutson, public works director at the biggest water and wastewater department in Minnesota. For Knutson, the real challenge was in overseeing software and plant monitoring upgrades to multiple plants with his own internal staff. System integrators weren’t in the budget.

“So, the real challenge is to maintain the different technologies during that timeframe,” says Knutson. We’re talking about the new and old versions of software running side-by-side at different plants or just at different plants.”

Even the Bluefield research report says that “a significant hurdle will be integrating legacy systems with new software platforms.” However, the challenge may be workflow processes, the less glamorous side of the asset management and IIoT narrative.

Other highlights from the research include:

• Halving non-revenue water– leaks and billing errors– and reducing energy consumption from 20% to 40%.

• The smart water sector is expected to scale to $12 billion in the US and $11 billion in Europe by 2025. Other hotspots for smart water activity include Australia, Singapore and Israel, where water stress and established utility network operators are more receptive to advanced technology adoption.

• European utilities are at the forefront of smart water in terms of operational solutions, while the US leads in terms of metering.

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155

6:49 pm
February 28, 2017
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Process Operators and Tools May Bridge the Gap to Predictive Maintenance

170228pmartin

Peter Reynolds, contributing analyst for ARC Advisory Group.

Jim Wentzel, dir of Global Reliability at General Mills has been on the conference circuit recently and has been discussing “contextuality” when it comes to manufacturing data in the food industry. In his discussions, Wentzel discusses General Mills “data journey” as a company — their own plants and contract manufacturing plants outside the enterprise — and is pushing for data transparency throughout the entire enterprise eco-system. That means various types of plant and enterprise data, such as plant floor , instrument, machine vibration, supply chain and even other plants mixed together to make efficient decisions.

That means a lot of business units — and external companies per Wentzel— coming together and possible changes in workforce responsibilities. One scenario would be to have process operators provide key insights on equipment health due to a better working knowledge and lifecycle history of a particular asset.

>> View More | Silicon Valley Company Joins the Predictive Maintenance Party

Peter Reynolds, contributing analyst for ARC Advisory Group discusses this scenario with his most recent post, “Predictive Maintenance or Predictive Operations?” Reynolds describes how operations can lean on better tools, processes and how condition-based monitoring goes only so far:

Both Prognostics and Condition-based monitoring are still reactive approaches and have been used widely for decades. Still, many companies struggle with making significant improvements in predicting failures and extending the life of critical assets.

He goes on to write:

Therefore one might come to the conclusion that any predictive maintenance or asset reliability strategy might begin with an overarching operations strategy and weigh heavily on the skills of the process engineer. The process engineer (and not the maintenance and reliability engineer), has the ability to interpret the process data across the spectrum of the process and any assets.

The rub is that operations, maintenance and even IT need to view enterprise via data in one IIoT platform, such as ThingWorx, Element Analytics, or many other offerings that can provide varying analytics to different groups.

>> To read the full post, click here

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154

7:05 pm
February 16, 2017
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Silicon Valley Company Joins the Predictive Maintenance Party

predictive maintenance platform

Source: Element Analytics

Silicon Valley-backed Element Analytics formally announced their industrial software analytics solution, Element Platform, to the market last month. The San Francisco-based Element Analytics is taking aim at the oil and gas, chemical, utility and mining industries while partnering with OSIsoft and Microsoft’s Partner Network.

The platform and the solution is a good fit for those industries, as those fields tend to rely on proprietary automation and equipment platforms that need optimization. Oil and gas, specifically, moved their strategy from offshore to their current installed base to find profitability and most producers are understanding the need for infrastructure improvement. From the press release, the Element Platform works with OSIsoft’s technology in moving unstructured, operational sensor data from “silos” to a cloud-based analytics platform, where asset models help predict downtime for physical equipment.

Related Content | How to Start a Predictive Maintenance Program

“Industrial operators face no shortage of data, says David Mount, Kleiner Perkins’ Green Growth Fund partner and co-founder of Element Analytics. Mounds of data exist, but getting the data to a ready state is core to making it analyzable, predictive and actionable.”

Predictive maintenance technology has been slow to be adopted due to operational and production conflicts, but recent IIoT solutions live on separate platforms. This allows for control platfom updates, like security patches to occur, while not interrupting asset management programs.

The Element platform also uses Microsoft Azure and Cortana Intelligence for the cloud-based analytics.

For more information, visit www.elementanalytics.com

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48

8:36 pm
February 9, 2017
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Analyze Big Data with Prescriptive Maintenance

By Grant Gerke, Contributing Editor

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at maintenancetechnology.com/iot.

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at maintenancetechnology.com/iot.

As manufacturers modernize plants and retrofit equipment with additional sensors, reliability and maintenance managers are working to develop Industrial Internet of Things (IIoT) strategies that effectively manage new data streams. In the past, manufacturers would employ operational consultants or specialists to analyze the mountains of data these sensors generate. Today, limited resources are driving reliability professionals to explore prescriptive maintenance.

Prescriptive maintenance is a component of the IIoT. This discipline uses machine learning and automated data review to prevent equipment or device failure. Some industry experts call it preventive maintenance with built-in intelligence.

It’s the next bridge for reliability teams to cross as referenced in the January 2017 edition of Maintenance Technology’s  On the Floor.” The focus was on regrets and hopes. One industry consultant stated, that among his clients, “the biggest regret seems to be PM/PdM compliance and not doing what they planned to do to prevent breakdowns.”

The consultant added, “that one client increased training and invested in maintenance employees but still hasn’t realized the returns on that investment.”

One reason for the lack of follow-through could be the ability to promptly act on plant-floor data, also known as perishable data in the field or factory floor.

Prescriptive maintenance allows reliability professionals to take preventive action quicker than conventional means or, in many cases, fully automates the process.

Prescriptive maintenance allows reliability professionals to take preventive action quicker than conventional means or, in many cases, fully automates the process.

In a recent article on 2017 IIoT trends, Abedayo Onigbanjo, director of marketing at Zebra Technologies, Lincolnshire, IL (zebra.com), stated “businesses must make sense of data before it expires. Enterprises are losing valuable insights with many disjointed sources generating and collecting data on their own, contributing to only bits and pieces of the big picture, instead of rendering a broad view.”

Legacy cultures and platforms are the main culprits. Take the process industries, for example. Many operations, including large chemical plants and oil fields, are relying on 4- to 20-mA fieldbus networking solutions. Identifying device defects is difficult in these facilities.

Procentec, Wateringen, The Netherlands (procentec.com), provides asset-management solutions that accelerate the identification of malfunctioning devices. In 2015, the company updated firmware for its Foundation Fieldbus Diagnostic module. The benefit was a “live list” of all operating devices in one overview, and device-type viewing in the oscilloscope images.

Documenting downtime is sometimes painful, but essential. “For a steel producer in Europe, the total estimated revenue for downtime was in the neighborhood of  [$1,600] 1,500€ a minute,” stated Matthew Dulcey, global sales manager for Procentec at a 2016 PROFI networking conference.

During the presentation, Dulcey also provided examples of how 1% of downtime for a plant running 24 hours/day equals about 78 hours of unscheduled downtime. According to Dulcey, a steel-plant maintenance team demonstrated that one preventive downtime event could pay for new diagnostic tools.

Other non-networking solutions for manufacturers include Panoramic Power’s (New York City, panpwr.com) Device Analyzer and its machine learning platform. This platform—PowerRadar version 2.0—learns usage patterns for devices in production lines and allows users to view an operational device state in real-time. The system collects device-level energy data, automatically learns device patterns and, after training, automatically identifies the different operational states of each instrument.

In the big picture for manufacturers, this is just the beginning for big data and IIoT. The road seems to lead to easy-to-use analytical tools and operational automation. MT

Grant Gerke is a business writer and content marketer in the manufacturing, power, and renewable-energy space. He has 15 years of experience covering industrial and field-automation areas.

237

3:56 pm
February 8, 2017
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How to Start a Predictive Maintenance Program

IIoT motorsDevice and equipment advances, on display in our MT IIoT web section, is past the early adoption stage, but operations and maintenance (O&M) teams are still wrapping their arms around predictive maintenance programs. A recent interview with ARC Advisory’s Ralph Rio via SAP’s Enterprise Asset Management discusses this very issue and more.

Excerpt below:

Q: So how do people begin moving toward predictive maintenance – how do they get there?

Ralph Rio: The first thing people need to do is to educate themselves to understand what is available from a technology standpoint. People just entering this area are no longer “early adopters” so there is plenty of information out there. Get comfortable with the platforms and the business processes.

Sometimes technology education is coming from your machine builder (OEM) with improved data acquisition capabilities. From this post, “Are Smaller IIoT Applications The Next Wave for End Users?” and discussion with Erl Campbell at Aventics, MT found out how this is working:

“By actually monitoring the spool position, the machine can track exactly how each valve performed during a motion cycle: where that valve started, whether it fully shifted or only partially shifted, and its final position. These data points help machine builders and end-user operators correct issues that may affect overall packaging quality and integrity,” the white paper states (written by Erl Campbell.

Campbell added in a recent interview that the company is working on whether the (valve) reliability data should communicate with the factory floor or maintenance. Is it going to be some kind of wireless communication or will techs plug into the manifold and download that data?

>> For more on how to create a predictive maintenance program with Ralph Rio

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134

5:11 am
February 2, 2017
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Big Data Challenge as Train Company Moves to Predictive Maintenance

maintenance costs

Trenitalia, an Italian train company, looks to reduce maintenance costs by 8 percent.

There’s an interesting blog series on Trenitalia, a state-owned Italian train company, via ARC’s Industrie 4.0 website that depicts a transition from condition monitoring to a more predictive approach. The company reveals its real-time dashboards, but also discusses their transition to a component-based maintenance approach, which has many parallels to the factory or field space.

The scope is impressive. The new predictive application includes up to 4,000 “rolling stock” assets, with each locomotive collects up to 10,000 parameters per second. According to a news report, sensors will measure variables such as motor temperature, speed, traction, braking effort and line voltage.rt and line voltage.

More from the News report:

Sensor data is aggregated on-board through a remote PC or similar interface and offloaded via a communication gateway, typically via wi-fi when a train arrives at a station or at the maintenance plant. Data is sent to a Trenitalia data centre, and loaded into SAP HANA and cloud systems and dashboards for real-time monitoring, analysis and drill-down.

From ARC:

However, the team identified more representative KPI’s than mileage. These include door opening/closing cycles. They distinguished groups of components with higher or lower risk. With this information, Trenitalia is transitioning to a dynamic, component-based maintenance strategy in which higher risk components and components reaching the limits of their KPIs are checked and maintained more frequently; while other components are checked and maintained less frequently. In some cases, diverging KPIs of components on the same train can be balanced by choosing specific destinations. For example, trips causing more left wheel rotations and accelerations can be balanced with destinations leading to more right wheel accelerations. Trenitalia had to make its integrated travel and maintenance schedules much more granular to achieve the desired massive increase in reliability and savings.

Read More of ARC’s Blog Series >>

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178

7:12 pm
January 25, 2017
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White Paper | Digital Prescriptive Maintenance

170125prescriptiveThe first wave of IIoT industry reports, about two years ago, included big claims and produced a lot of head scratching by manufacturers and Original Equipment Manufacturers (OEMs) due to the lack of actual applications. Two years later, pilot projects are producing results and next discipline to catch on is prescriptive maintenance.

The best way to describe prescriptive maintenance is “preventive maintenance technology with built-in intelligence, with the ultimate goal to minimize machine downtime. Below is a white paper, titled, “Digital Prescriptive Maintenance,” and it shows how sensors and diagnostics play their part with a prescriptive maintenance approach.

From the White Paper:

Prescriptive maintenance goes beyond the realm of productive, preventive, and predictive maintenance. Descriptive focuses on what happened in the past. Predictive analytics discovers potential options for the future. Prescriptive maintenance leverages all these approaches and capabilities. The realm of what should happen and the execution of optimized maintenance strategies is precisely the realm of prescriptive maintenance. With prescriptive maintenance, devices, in collaboration with operators, are proactive participants in their own maintenance.

>> Download the White Paper

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